Cognitive function is significantly impacted by Alzheimer’s disease, a progressive neurological disease. Early and precise detection is essential for treatment and intervention that works. This paper suggests a comprehensive preprocessing method for the Alzheimer’s Disease Neuroimaging Initiative picture collection in order to enhance Alzheimer’s disease identification. The proposed technique includes preprocessing pipeline phases such as image registration, the process of segmentation the normalization process, and bias field correction. These tactics facilitate the process of accurately and consistently extracting relevant information from the imaging data by consolidating and improving it. Transformed data with the appropriate properties is necessary for subsequent prediction models to be able to classify and predict the course of Alzheimer disease. The results demonstrate that the recommended preprocessing technique significantly improves the quality and reliability of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, enhancing the sensitivity and accuracy of the initial Alzheimer’s diagnosis.